This is part two in our AI-Ready Data Pipelines series. Read part one here.
Having a first-party data collection endpoint does not leave you home and dry, however. If you want to get to know your users, you need to establish a relationship and earn their trust, and that means delivering value while being extremely respectful with what you know about them. When it comes to user data, particularly in the feeding grounds of AI, it is vital to ensure that consent is obtained explicitly and all the data that is used has been cleared to be employed for that purpose. And keep in mind that consent is not eternal! Just because you have consent today does not mean you will have consent tomorrow—and all the data you collect will need to go, so a good strategy to keep your datasets fresh and healthy is mandatory.
Identity
The first step towards compliant data is good identity management. Every data tool in the market sets its unique user identifier, usually stored in cookies, which can make keeping track of specific user data more complicated as you add new services to your data stack, especially if you collaborate with third-party agencies. However, once you have a first-party data collector, a fun initial exercise is to provision your very own user identities that you can then use to create a relationship between your assigned identity and any additional identifiers that are available, enabling you to track which data is stored where at all times. This also applies to the data you collect directly from users. If they change their preferences regarding the use of their data for audience targeting, personalization, or participation in experimentation, it presents an intriguing problem for your data engineers, rather than a daunting task for your marketers and data analysts.
Compliance
This brings us to the subject of compliance. Regarding compliant use of data, it is important to ensure that data cannot be misused. For example, if I export all my Google Analytics 4 data to a BigQuery table, how can I ensure that the data will be used only for personalization and not marketing? To this end, creating datasets for specific purposes brings several advantages:
- Access control – Make sure only the people involved with a specific purpose (newsletter, content recommendation, fraud detection, etc.) have access to that data, limiting the possibility the data can be misused.
- Scope – Specific purposes require specific data, so including only the necessary data will make using it quicker and less risky.
- Efficiency – As you pipe data into a specific dataset you can format it according to the requirements of the data consumers, enabling them to use it more efficiently. Nobody likes data cleanup!
- Data enrichment – Data that comes from an application is usually—and should be—frugal to ensure timely delivery and optimal use of network calls. Collected data can be enriched and complemented with data stored in your backend to create a more complete dataset.
Having a first-party data collector makes it very easy to create purpose-specific datasets, allowing you to tap into your data firehose and extract the data you need, transform it to the data you want, and store it where it is most convenient. Add to that a good solid identity management system with mechanisms to keep your data clean and up-to-date and you will have an efficient, governable, and compliant data repository.
Join us for part three to see how to make sure that you are consuming the most trustworthy data, or review part one if you have any doubts about first-party data collection.
